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20202024
most citedSemi-Supervised Deep Ensembles for Blind Image Quality Assessment

7 citations · 16 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.CV2024

Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures

Jiaqi He, Zhihua Wang, Leon Wang +4

Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a ``perceptually uniform'' color space, or features…

cs.CV2023

Learning a Deep Color Difference Metric for Photographic Images

Haoyu Chen, Zhihua Wang, Yang Yang +2

Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photog…

cs.CV2022

Measuring Perceptual Color Differences of Smartphone Photographs

Zhihua Wang, Keshuo Xu, Yang Yang +5

Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography. Despite the long history, most CD measures have been constrained by psychophys…

cs.CV2021★ 7 cited

Semi-Supervised Deep Ensembles for Blind Image Quality Assessment

Zhihua Wang, Dingquan Li, Kede Ma

Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse." Here we investigate a semi-supervised ensem…

cs.CV2021★ 2 cited

Troubleshooting Blind Image Quality Models in the Wild

Zhihua Wang, Haotao Wang, Tianlong Chen +2

Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics. When…